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Deep Reinforcement Learning Hands-On

You're reading from Deep Reinforcement Learning Hands-On Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788834247

Length 546 pages

Edition 1st Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Maxim Lapan

View More author details

Table of Contents (21) Chapters

Preface

1. What is Reinforcement Learning? FREE CHAPTER

2. OpenAI Gym

3. Deep Learning with PyTorch

4. The Cross-Entropy Method

5. Tabular Learning and the Bellman Equation

6. Deep Q-Networks

7. DQN Extensions

8. Stocks Trading Using RL

9. Policy Gradients – An Alternative

10. The Actor-Critic Method

11. Asynchronous Advantage Actor-Critic

12. Chatbots Training with RL

13. Web Navigation

14. Continuous Action Space

15. Trust Regions – TRPO, PPO, and ACKTR

16. Black-Box Optimization in RL

17. Beyond Model-Free – Imagination

18. AlphaGo Zero

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Index

References

Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver, 2017, Rainbow: Combining Improvements in Deep Reinforcement Learning. arXiv:1710.02298
Sutton, R.S. 1988, Learning to Predict by the Methods of Temporal Differences, Machine Learning 3(1):9-44
Hado Van Hasselt, Arthur Guez, David Silver, 2015, Deep Reinforcement Learning with Double Q-Learning. arXiv:1509.06461v3
Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Pilot, Jacob Menick, Ian Osband, Alex Graves, Vlad Mnih, Remi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg, 2017, Noisy Networks for Exploration arXiv:1706.10295v1
Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaus, David Saxton, Remi Munos 2016, Unifying Count-Based Exploration and Intrinsic Motivation arXiv:1606.01868v2
Jarryd Martin, Suraj Narayanan Sasikumar, Tom Everitt, Marcus Hutter, 2017, Count-Based Exploration in Feature Space for Reinforcement...

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Authors (1)

Maxim Lapan

Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.

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